Laboratory of Image Processing
3D Slicer License
Yes
University of Valladolid
NITRC
Joint Anisotropic LMMSE Filter for Stationary Rician noise removal in DWI
MacOS, POSIX/UNIX-like, Microsoft
Yes
C++
This module reduces Rician noise on DWI. It filters the image in the mean squared error sense using a Rician noise model. The N closest gradient directions to the direction being processed are filtered together to improve the results: the noise-free signal is seen as an n-dimensional vector which has to be estimated with the LMMSE method from a set of corrupted measurements. The covariance matrix of the noise-free vector and the cross covariance between this signal and the noise have to be estimated, which is done taking into account the image formation process.
All these estimations are performed as sample estimates in a 'shaped neighborhood' defined by the weights extracted from the structural similarity of the voxels following the same idea as in the Non-Local Means filter.
This C++ code can be compiled as either a standalone (ITK required. Versions 3 and 4 are supported) or as a CLI module for 3-D Slicer (versions 3 and 4). Only "nhdr/nrrd" DWIs are supported.
2013-10-18
Whole package with source code, multi-platform binaries, Matlab/Octave code, and test data
2013-10-18
Matlab/Octave code with demo
2013-2-04
Beta 1.0
Joint Anisotropic LMMSE Filter for Stationary Rician noise removal in DWI
Intensity Non-uniformity Correction, Low Pass, Band Pass, High Pass, Artifact Removal, Quality Metrics, Spatial Convolution - Deconvolution, MR, Computational Neuroscience, Clinical Neuroinformatics, Console (Text Based), End Users, 3D Slicer License, English, MacOS, POSIX/UNIX-like, Microsoft, C++, Nrrd
http://www.nitrc.org/projects/jalmmse_dwi/, http://www.nitrc.org/projects/jalmmse_dwi/